We lifted 6 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| cure-lab/SCINet | canonical | 1 of 1 |
| HiddeKanger/SCINet | pwc_unofficial | 1 of 5 |
| Function | Status | Where it lives |
|---|---|---|
| get_variable | Ran | cure-lab/SCINet/models/SCINet.py code served (permissive licence) · get_code("f4796d7c5da2f9ca") |
| unique_cols | Ran | HiddeKanger/SCINet/base/preprocess_data.py code served (permissive licence) · get_code("c880687cb3cf3e16") |
| match_data | Not yet run | HiddeKanger/SCINet/exp/live_trading/utils/live_scinet.py code served (permissive licence) · get_code("8428bb3b71dee6e6") |
| match_data | Not yet run | HiddeKanger/SCINet/base/preprocess_data.py code served (permissive licence) · get_code("e3f6538295dc7600") |
| preprocess | Not yet run | HiddeKanger/SCINet/base/preprocess_data.py code served (permissive licence) · get_code("2c4032b3c091dc65") |
| scinet_builder | Not yet run | HiddeKanger/SCINet/base/SCINet.py code served (permissive licence) · get_code("1a7780c7bde63e23") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
One unique property of time series is that the temporal relations are largely preserved after downsampling into two sub-sequences. By taking advantage of this property, we propose a novel neural network architecture that conducts sample convolution and interaction for temporal modeling and forecasting, named SCINet. Specifically, SCINet is a recursive downsample-convolve-interact architecture. In each layer, we use multiple convolutional filters to extract distinct yet valuable temporal features from the downsampled sub-sequences or features. By combining these rich features aggregated from multiple resolutions, SCINet effectively models time series with complex temporal dynamics. Experimental results show that SCINet achieves significant forecasting accuracy improvements over both existing convolutional models and Transformer-based solutions across various real-world time series forecasting datasets. Our codes and data are available at https://github.com/cure-lab/SCINet.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2106.09305")
get_code_for_paper("2106.09305")
have("2106.09305")
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